import numpy as np
import pandas as pd
df=pd.read_csv(r'/Users/binodrai/Desktop/1718991964134-9073-customer_churn_data.csv')

df.head(5)

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	CUST0000	Male	0	No	Yes	23	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Bank transfer	49.85	1146.55	No
1	CUST0001	Female	0	Yes	No	43	No	No phone service	DSL	Yes	...	Yes	No	Yes	No	Month-to-month	No	Mailed check	100.70	4330.10	Yes
2	CUST0002	Male	1	No	No	51	Yes	No	DSL	No	...	Yes	Yes	No	No	One year	No	Electronic check	97.33	4963.83	Yes
3	CUST0003	Male	1	No	No	72	Yes	Yes	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Credit card	101.38	7299.36	No
4	CUST0004	Male	1	No	No	25	Yes	Yes	DSL	No	...	No	Yes	No	Yes	Month-to-month	No	Electronic check	52.22	1305.50	Yes

5 rows × 21 columns

df.tail()

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
5875	CUST5875	Male	0	Yes	Yes	71	Yes	No	DSL	Yes	...	No	Yes	No	No	Month-to-month	Yes	Mailed check	74.21	5268.91	No
5876	CUST5876	Male	0	No	No	22	Yes	Yes	DSL	No	...	Yes	Yes	No	Yes	One year	No	Mailed check	65.43	1439.46	Yes
5877	CUST5877	Female	0	No	No	68	No	No phone service	Fiber optic	Yes	...	Yes	Yes	Yes	No	One year	Yes	Bank transfer	59.78	4065.04	No
5878	CUST5878	Female	0	Yes	Yes	14	No	No phone service	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Month-to-month	Yes	Mailed check	91.88	1286.32	No
5879	CUST5879	Female	1	Yes	No	23	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	25.45	585.35	Yes

5 rows × 21 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5880 entries, 0 to 5879
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        5880 non-null   object 
 1   gender            5880 non-null   object 
 2   SeniorCitizen     5880 non-null   int64  
 3   Partner           5880 non-null   object 
 4   Dependents        5880 non-null   object 
 5   tenure            5880 non-null   int64  
 6   PhoneService      5880 non-null   object 
 7   MultipleLines     5880 non-null   object 
 8   InternetService   5880 non-null   object 
 9   OnlineSecurity    5880 non-null   object 
 10  OnlineBackup      5880 non-null   object 
 11  DeviceProtection  5880 non-null   object 
 12  TechSupport       5880 non-null   object 
 13  StreamingTV       5880 non-null   object 
 14  StreamingMovies   5880 non-null   object 
 15  Contract          5880 non-null   object 
 16  PaperlessBilling  5880 non-null   object 
 17  PaymentMethod     5880 non-null   object 
 18  MonthlyCharges    5880 non-null   float64
 19  TotalCharges      5880 non-null   float64
 20  Churn             5880 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 964.8+ KB

df.describe()

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges
count	5880.000000	5880.000000	5880.000000	5880.000000
mean	0.500680	36.549150	70.157779	2566.813165
std	0.500042	20.909674	28.804615	1910.017743
min	0.000000	1.000000	20.000000	20.030000
25%	0.000000	18.000000	45.717500	1020.217500
50%	1.000000	37.000000	70.155000	2136.445000
75%	1.000000	55.000000	95.457500	3767.665000
max	1.000000	72.000000	119.990000	8589.600000

df.shape

(5880, 21)

df.dtypes

customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges        float64
Churn                object
dtype: object

df.nunique()

customerID          5880
gender                 2
SeniorCitizen          2
Partner                2
Dependents             2
tenure                72
PhoneService           2
MultipleLines          3
InternetService        3
OnlineSecurity         3
OnlineBackup           3
DeviceProtection       3
TechSupport            3
StreamingTV            3
StreamingMovies        3
Contract               3
PaperlessBilling       2
PaymentMethod          4
MonthlyCharges      4439
TotalCharges        5794
Churn                  2
dtype: int64

df['gender'].value_counts(ascending=True)

gender
Female    2930
Male      2950
Name: count, dtype: int64

df.isnull().sum()  #detect missing value in dataset

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

df.nunique() #to know unique entries in categoraical column,understanding range of categories present

customerID          5880
gender                 2
SeniorCitizen          2
Partner                2
Dependents             2
tenure                72
PhoneService           2
MultipleLines          3
InternetService        3
OnlineSecurity         3
OnlineBackup           3
DeviceProtection       3
TechSupport            3
StreamingTV            3
StreamingMovies        3
Contract               3
PaperlessBilling       2
PaymentMethod          4
MonthlyCharges      4439
TotalCharges        5794
Churn                  2
dtype: int64

df.columns

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

correlation = df['MonthlyCharges'].corr(df['TotalCharges']).round(5)#only between numerical columns
print(f"Correlation between MonthlyCharges and TotalCharges: {correlation}")

Correlation between MonthlyCharges and TotalCharges: 0.5567

drop_column=df.drop('PaperlessBilling',axis=1)#axis 1 represent column have to specify drop in column or row

df.dtypes

customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges        float64
Churn                 int64
dtype: object

sort_Totalcharges=df.sort_values(by='TotalCharges',ascending=False)# can i show just total charges?
a=sort_Totalcharges['TotalCharges']
a.head()

1765    8589.60
5082    8505.09
410     8498.16
1362    8478.00
5349    8449.20
Name: TotalCharges, dtype: float64

df.sample(2)

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
2190	CUST2190	Female	0	Yes	No	9	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	One year	Yes	Mailed check	38.31	344.79	1
3221	CUST3221	Female	0	Yes	Yes	65	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card	103.71	6741.15	1

2 rows × 21 columns

grouped=df.groupby('gender').agg({'MonthlyCharges':'mean','tenure':'count'})
print(grouped)

        MonthlyCharges  tenure
gender                        
Female       69.888218    2930
Male         70.425512    2950

df.fillna(value=0, inplace=True)  # Replace NaN with 0
print(df)

     customerID  gender  SeniorCitizen Partner Dependents  tenure  \
0      CUST0000    Male              0      No        Yes      23   
1      CUST0001  Female              0     Yes         No      43   
2      CUST0002    Male              1      No         No      51   
3      CUST0003    Male              1      No         No      72   
4      CUST0004    Male              1      No         No      25   
...         ...     ...            ...     ...        ...     ...   
5875   CUST5875    Male              0     Yes        Yes      71   
5876   CUST5876    Male              0      No         No      22   
5877   CUST5877  Female              0      No         No      68   
5878   CUST5878  Female              0     Yes        Yes      14   
5879   CUST5879  Female              1     Yes         No      23   

     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \
0              No  No phone service              No  No internet service  ...   
1              No  No phone service             DSL                  Yes  ...   
2             Yes                No             DSL                   No  ...   
3             Yes               Yes             DSL                  Yes  ...   
4             Yes               Yes             DSL                   No  ...   
...           ...               ...             ...                  ...  ...   
5875          Yes                No             DSL                  Yes  ...   
5876          Yes               Yes             DSL                   No  ...   
5877           No  No phone service     Fiber optic                  Yes  ...   
5878           No  No phone service     Fiber optic                  Yes  ...   
5879          Yes                No              No  No internet service  ...   

         DeviceProtection          TechSupport          StreamingTV  \
0     No internet service  No internet service  No internet service   
1                     Yes                   No                  Yes   
2                     Yes                  Yes                   No   
3                     Yes                   No                   No   
4                      No                  Yes                   No   
...                   ...                  ...                  ...   
5875                   No                  Yes                   No   
5876                  Yes                  Yes                   No   
5877                  Yes                  Yes                  Yes   
5878                  Yes                  Yes                  Yes   
5879  No internet service  No internet service  No internet service   

          StreamingMovies        Contract PaperlessBilling     PaymentMethod  \
0     No internet service  Month-to-month              Yes     Bank transfer   
1                      No  Month-to-month               No      Mailed check   
2                      No        One year               No  Electronic check   
3                      No  Month-to-month               No       Credit card   
4                     Yes  Month-to-month               No  Electronic check   
...                   ...             ...              ...               ...   
5875                   No  Month-to-month              Yes      Mailed check   
5876                  Yes        One year               No      Mailed check   
5877                   No        One year              Yes     Bank transfer   
5878                  Yes  Month-to-month              Yes      Mailed check   
5879  No internet service  Month-to-month               No      Mailed check   

     MonthlyCharges  TotalCharges  Churn  
0             49.85       1146.55      0  
1            100.70       4330.10      1  
2             97.33       4963.83      1  
3            101.38       7299.36      0  
4             52.22       1305.50      1  
...             ...           ...    ...  
5875          74.21       5268.91      0  
5876          65.43       1439.46      1  
5877          59.78       4065.04      0  
5878          91.88       1286.32      0  
5879          25.45        585.35      1  

[5880 rows x 21 columns]

df.isna()

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5875	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
5876	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
5877	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
5878	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
5879	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

5880 rows × 21 columns

df.fillna(value=0, inplace=True)  # Replace NaN with 0
print(df) #there no nan value present here

     customerID  gender  SeniorCitizen Partner Dependents  tenure  \
0      CUST0000    Male              0      No        Yes      23   
1      CUST0001  Female              0     Yes         No      43   
2      CUST0002    Male              1      No         No      51   
3      CUST0003    Male              1      No         No      72   
4      CUST0004    Male              1      No         No      25   
...         ...     ...            ...     ...        ...     ...   
5875   CUST5875    Male              0     Yes        Yes      71   
5876   CUST5876    Male              0      No         No      22   
5877   CUST5877  Female              0      No         No      68   
5878   CUST5878  Female              0     Yes        Yes      14   
5879   CUST5879  Female              1     Yes         No      23   

     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \
0              No  No phone service              No  No internet service  ...   
1              No  No phone service             DSL                  Yes  ...   
2             Yes                No             DSL                   No  ...   
3             Yes               Yes             DSL                  Yes  ...   
4             Yes               Yes             DSL                   No  ...   
...           ...               ...             ...                  ...  ...   
5875          Yes                No             DSL                  Yes  ...   
5876          Yes               Yes             DSL                   No  ...   
5877           No  No phone service     Fiber optic                  Yes  ...   
5878           No  No phone service     Fiber optic                  Yes  ...   
5879          Yes                No              No  No internet service  ...   

         DeviceProtection          TechSupport          StreamingTV  \
0     No internet service  No internet service  No internet service   
1                     Yes                   No                  Yes   
2                     Yes                  Yes                   No   
3                     Yes                   No                   No   
4                      No                  Yes                   No   
...                   ...                  ...                  ...   
5875                   No                  Yes                   No   
5876                  Yes                  Yes                   No   
5877                  Yes                  Yes                  Yes   
5878                  Yes                  Yes                  Yes   
5879  No internet service  No internet service  No internet service   

          StreamingMovies        Contract PaperlessBilling     PaymentMethod  \
0     No internet service  Month-to-month              Yes     Bank transfer   
1                      No  Month-to-month               No      Mailed check   
2                      No        One year               No  Electronic check   
3                      No  Month-to-month               No       Credit card   
4                     Yes  Month-to-month               No  Electronic check   
...                   ...             ...              ...               ...   
5875                   No  Month-to-month              Yes      Mailed check   
5876                  Yes        One year               No      Mailed check   
5877                   No        One year              Yes     Bank transfer   
5878                  Yes  Month-to-month              Yes      Mailed check   
5879  No internet service  Month-to-month               No      Mailed check   

     MonthlyCharges  TotalCharges  Churn  
0             49.85       1146.55      0  
1            100.70       4330.10      1  
2             97.33       4963.83      1  
3            101.38       7299.36      0  
4             52.22       1305.50      1  
...             ...           ...    ...  
5875          74.21       5268.91      0  
5876          65.43       1439.46      1  
5877          59.78       4065.04      0  
5878          91.88       1286.32      0  
5879          25.45        585.35      1  

[5880 rows x 21 columns]

df['Partner'].replace({'Yes': 1, 'No': 0}, inplace=True)  # Replace 'Yes' with 1 and 'No' with 0 in 'Partner' column
print(df['Partner'])

0       0
1       1
2       0
3       0
4       0
       ..
5875    1
5876    0
5877    0
5878    1
5879    1
Name: Partner, Length: 5880, dtype: int64

df.describe()

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges
count	5880.000000	5880.000000	5880.000000	5880.000000
mean	0.500680	36.549150	70.157779	2565.315153
std	0.500042	20.909674	28.804615	1905.670601
min	0.000000	1.000000	20.000000	20.030000
25%	0.000000	18.000000	45.717500	1020.217500
50%	1.000000	37.000000	70.155000	2136.445000
75%	1.000000	55.000000	95.457500	3767.665000
max	1.000000	72.000000	119.990000	7888.836250

df.index

RangeIndex(start=0, stop=5880, step=1)

import plotly.graph_objects as go

# Define index for x-axis
x = df.index

# Create line plots for each column
fig = go.Figure()

# Plot SeniorCitizen
fig.add_trace(go.Scatter(x=x, y=df['SeniorCitizen'], mode='lines+markers', name='SeniorCitizen'))

# Plot Tenure
fig.add_trace(go.Scatter(x=x, y=df['tenure'], mode='lines+markers', name='Tenure'))

# Plot MonthlyCharges
fig.add_trace(go.Scatter(x=x, y=df['MonthlyCharges'], mode='lines+markers', name='MonthlyCharges'))

# Plot TotalCharges
fig.add_trace(go.Scatter(x=x, y=df['TotalCharges'], mode='lines+markers', name='TotalCharges'))

# Customize plot
fig.update_layout(title='DataFrame Line Plots', xaxis_title='Index', yaxis_title='Values', legend_title='Columns')

# Show plot
fig.show()

import pandas as pd
import matplotlib.pyplot as plt
# Cross-tabulation of Gender vs Review
gender_review_ct = pd.crosstab(df['gender'], df['InternetService'])
print(gender_review_ct)
gender_review_ct.plot(kind='bar', stacked=True, title='Usage of Internet Services By Gender')
plt.xlabel('gender')
plt.ylabel('Count')
plt.show()

InternetService  DSL  Fiber optic    No
gender                                 
Female           938          957  1035
Male             998          958   994

df.rename(columns={'gender':'Sex'})

	customerID	Sex	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	CUST0000	Male	0	No	Yes	23	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Bank transfer	49.85	1146.55	No
1	CUST0001	Female	0	Yes	No	43	No	No phone service	DSL	Yes	...	Yes	No	Yes	No	Month-to-month	No	Mailed check	100.70	4330.10	Yes
2	CUST0002	Male	1	No	No	51	Yes	No	DSL	No	...	Yes	Yes	No	No	One year	No	Electronic check	97.33	4963.83	Yes
3	CUST0003	Male	1	No	No	72	Yes	Yes	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Credit card	101.38	7299.36	No
4	CUST0004	Male	1	No	No	25	Yes	Yes	DSL	No	...	No	Yes	No	Yes	Month-to-month	No	Electronic check	52.22	1305.50	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5875	CUST5875	Male	0	Yes	Yes	71	Yes	No	DSL	Yes	...	No	Yes	No	No	Month-to-month	Yes	Mailed check	74.21	5268.91	No
5876	CUST5876	Male	0	No	No	22	Yes	Yes	DSL	No	...	Yes	Yes	No	Yes	One year	No	Mailed check	65.43	1439.46	Yes
5877	CUST5877	Female	0	No	No	68	No	No phone service	Fiber optic	Yes	...	Yes	Yes	Yes	No	One year	Yes	Bank transfer	59.78	4065.04	No
5878	CUST5878	Female	0	Yes	Yes	14	No	No phone service	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Month-to-month	Yes	Mailed check	91.88	1286.32	No
5879	CUST5879	Female	1	Yes	No	23	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	25.45	585.35	Yes

5880 rows × 21 columns

df_2= df.loc[:,['MonthlyCharges','TotalCharges']]
df_2.head()

	MonthlyCharges	TotalCharges
0	49.85	1146.55
1	100.70	4330.10
2	97.33	4963.83
3	101.38	7299.36
4	52.22	1305.50

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(df_2)
df_scaled = scaler.transform(df_2)
df_scaled

array([[-0.7050782 , -0.74364961],
       [ 1.06041406,  0.92325676],
       [ 0.94340897,  1.25507771],
       ...,
       [-0.36031246,  0.78447138],
       [ 0.75418708, -0.67046606],
       [-1.55223673, -1.03749386]])

df_scaled = pd.DataFrame(df_scaled)

df_scaled.describe().round(0)

	0	1
count	5880.0	5880.0
mean	0.0	0.0
std	1.0	1.0
min	-2.0	-1.0
25%	-1.0	-1.0
50%	-0.0	-0.0
75%	1.0	1.0
max	2.0	3.0

from sklearn.preprocessing import MinMaxScaler
minmax = MinMaxScaler()
minmax.fit(df_2)#also renaming data frame
df_minmax = minmax.transform(df_2)
df_minmax

array([[0.29852985, 0.13145584],
       [0.80708071, 0.50295056],
       [0.77337734, 0.57690176],
       ...,
       [0.39783978, 0.47202018],
       [0.71887189, 0.14776587],
       [0.05450545, 0.0659683 ]])

df_minmax=pd.DataFrame(df_minmax)
df_minmax.describe()

	0	1
count	5880.000000	5880.000000
mean	0.501628	0.297189
std	0.288075	0.222884
min	0.000000	0.000000
25%	0.257201	0.116714
50%	0.501600	0.246969
75%	0.754650	0.437319
max	1.000000	1.000000

df.head()

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	CUST0000	Male	0	No	Yes	23	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Bank transfer	49.85	1146.55	No
1	CUST0001	Female	0	Yes	No	43	No	No phone service	DSL	Yes	...	Yes	No	Yes	No	Month-to-month	No	Mailed check	100.70	4330.10	Yes
2	CUST0002	Male	1	No	No	51	Yes	No	DSL	No	...	Yes	Yes	No	No	One year	No	Electronic check	97.33	4963.83	Yes
3	CUST0003	Male	1	No	No	72	Yes	Yes	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Credit card	101.38	7299.36	No
4	CUST0004	Male	1	No	No	25	Yes	Yes	DSL	No	...	No	Yes	No	Yes	Month-to-month	No	Electronic check	52.22	1305.50	Yes

5 rows × 21 columns

method_counts = df['PaymentMethod'].value_counts()

method_counts

PaymentMethod
Credit card         1503
Electronic check    1488
Mailed check        1462
Bank transfer       1427
Name: count, dtype: int64

df.dtypes

customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges        float64
Churn                object
dtype: object

pd.get_dummies(df['PaymentMethod'],drop_first=True).astype(int).head(10)

	Credit card	Electronic check	Mailed check
0	0	0	0
1	0	0	1
2	0	1	0
3	1	0	0
4	0	1	0
5	1	0	0
6	0	0	0
7	0	0	1
8	0	0	1
9	0	1	0

pd.get_dummies(df['PaymentMethod'],).astype(int).head(10)

	Bank transfer	Credit card	Electronic check	Mailed check
0	1	0	0	0
1	0	0	0	1
2	0	0	1	0
3	0	1	0	0
4	0	0	1	0
5	0	1	0	0
6	1	0	0	0
7	0	0	0	1
8	0	0	0	1
9	0	0	1	0

df

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	CUST0000	Male	0	No	Yes	23	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Bank transfer	49.85	1146.55	No
1	CUST0001	Female	0	Yes	No	43	No	No phone service	DSL	Yes	...	Yes	No	Yes	No	Month-to-month	No	Mailed check	100.70	4330.10	Yes
2	CUST0002	Male	1	No	No	51	Yes	No	DSL	No	...	Yes	Yes	No	No	One year	No	Electronic check	97.33	4963.83	Yes
3	CUST0003	Male	1	No	No	72	Yes	Yes	DSL	Yes	...	Yes	No	No	No	Month-to-month	No	Credit card	101.38	7299.36	No
4	CUST0004	Male	1	No	No	25	Yes	Yes	DSL	No	...	No	Yes	No	Yes	Month-to-month	No	Electronic check	52.22	1305.50	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5875	CUST5875	Male	0	Yes	Yes	71	Yes	No	DSL	Yes	...	No	Yes	No	No	Month-to-month	Yes	Mailed check	74.21	5268.91	No
5876	CUST5876	Male	0	No	No	22	Yes	Yes	DSL	No	...	Yes	Yes	No	Yes	One year	No	Mailed check	65.43	1439.46	Yes
5877	CUST5877	Female	0	No	No	68	No	No phone service	Fiber optic	Yes	...	Yes	Yes	Yes	No	One year	Yes	Bank transfer	59.78	4065.04	No
5878	CUST5878	Female	0	Yes	Yes	14	No	No phone service	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Month-to-month	Yes	Mailed check	91.88	1286.32	No
5879	CUST5879	Female	1	Yes	No	23	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	25.45	585.35	Yes

5880 rows × 21 columns

df.describe().round(2)

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges
count	5880.0	5880.00	5880.00	5880.00
mean	0.5	36.55	70.16	2566.81
std	0.5	20.91	28.80	1910.02
min	0.0	1.00	20.00	20.03
25%	0.0	18.00	45.72	1020.22
50%	1.0	37.00	70.16	2136.44
75%	1.0	55.00	95.46	3767.66
max	1.0	72.00	119.99	8589.60

upper_limit = df['tenure'].mean() + 3*df['tenure'].std()
lower_limit = df['tenure'].mean() - 3*df['tenure'].std()

upper_limit

99.27817099498411

lower_limit

-26.179871675256223

Outlier_zscore = df[(df['tenure'] >=upper_limit)|(df['tenure'] <=lower_limit)]
Outlier_zscore

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn

0 rows × 21 columns

upper_limit = df['TotalCharges'].mean() + 3*df['TotalCharges'].std()
lower_limit = df['TotalCharges'].mean() - 3*df['TotalCharges'].std()

upper_limit

8296.866392560543

lower_limit

-3163.2400626285716

Outlier_zscore = df[(df['TotalCharges'] >=upper_limit)|(df['TotalCharges'] <=lower_limit)]
Outlier_zscore

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
410	CUST0410	Male	1	No	Yes	72	No	No phone service	Fiber optic	Yes	...	Yes	No	No	No	Month-to-month	Yes	Credit card	118.03	8498.16	Yes
764	CUST0764	Male	1	Yes	No	71	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Electronic check	118.58	8419.18	No
1006	CUST1006	Female	0	Yes	No	72	No	No phone service	DSL	No	...	No	Yes	Yes	Yes	Two year	Yes	Mailed check	116.53	8390.16	No
1362	CUST1362	Male	0	No	No	72	Yes	Yes	DSL	No	...	No	Yes	Yes	No	Two year	No	Credit card	117.75	8478.00	Yes
1765	CUST1765	Female	0	Yes	No	72	Yes	Yes	Fiber optic	Yes	...	Yes	Yes	No	Yes	One year	Yes	Bank transfer	119.30	8589.60	No
5082	CUST5082	Male	0	No	No	71	Yes	No	Fiber optic	Yes	...	No	No	No	Yes	One year	Yes	Bank transfer	119.79	8505.09	No
5349	CUST5349	Male	1	No	Yes	72	Yes	No	Fiber optic	No	...	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card	117.35	8449.20	No
5476	CUST5476	Male	1	Yes	No	71	Yes	Yes	DSL	No	...	Yes	No	Yes	No	One year	Yes	Mailed check	118.74	8430.54	No

8 rows × 21 columns

trimmed_df = df[(df['TotalCharges'] <=upper_limit) & (df['TotalCharges'] >=lower_limit)] #trimming using Z-score
trimmed_df.shape

(5872, 21)

df=pd.read_csv(r'/Users/binodrai/Desktop/1718991964134-9073-customer_churn_data.csv')

# IQR METHOD ::Calculate Q1 (25th percentile) and Q3 (75th percentile)
Q1 = df['TotalCharges'].quantile(0.25)
Q3 = df['TotalCharges'].quantile(0.75)
IQR = Q3 - Q1

# Define lower and upper bounds for outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

lower_bound

-3100.9537500000006

upper_bound

7888.83625

###Outlier IQR score 
Outlier_zscore
Outlier_iqr = df[(df['TotalCharges'] >=upper_bound)|(df['TotalCharges'] <=lower_bound)]
Outlier_iqr

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
83	CUST0083	Female	1	Yes	No	71	Yes	Yes	Fiber optic	Yes	...	No	Yes	No	No	One year	No	Bank transfer	112.54	7990.34	Yes
410	CUST0410	Male	1	No	Yes	72	No	No phone service	Fiber optic	Yes	...	Yes	No	No	No	Month-to-month	Yes	Credit card	118.03	8498.16	Yes
650	CUST0650	Male	0	Yes	Yes	72	No	No phone service	Fiber optic	No	...	No	Yes	No	Yes	Month-to-month	Yes	Mailed check	111.87	8054.64	No
764	CUST0764	Male	1	Yes	No	71	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Electronic check	118.58	8419.18	No
1006	CUST1006	Female	0	Yes	No	72	No	No phone service	DSL	No	...	No	Yes	Yes	Yes	Two year	Yes	Mailed check	116.53	8390.16	No
1362	CUST1362	Male	0	No	No	72	Yes	Yes	DSL	No	...	No	Yes	Yes	No	Two year	No	Credit card	117.75	8478.00	Yes
1598	CUST1598	Female	1	No	Yes	68	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Electronic check	118.03	8026.04	No
1631	CUST1631	Male	1	No	Yes	69	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Bank transfer	115.61	7977.09	No
1765	CUST1765	Female	0	Yes	No	72	Yes	Yes	Fiber optic	Yes	...	Yes	Yes	No	Yes	One year	Yes	Bank transfer	119.30	8589.60	No
1976	CUST1976	Male	0	No	No	67	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Mailed check	118.63	7948.21	No
2218	CUST2218	Female	1	No	Yes	70	No	No phone service	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Electronic check	117.34	8213.80	Yes
2225	CUST2225	Male	0	Yes	No	69	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Credit card	117.39	8099.91	Yes
2314	CUST2314	Male	1	No	Yes	72	Yes	No	Fiber optic	No	...	No	No	Yes	No	One year	Yes	Bank transfer	111.85	8053.20	Yes
2818	CUST2818	Male	1	Yes	No	67	Yes	Yes	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Two year	Yes	Electronic check	118.89	7965.63	No
3060	CUST3060	Female	1	No	No	70	No	No phone service	Fiber optic	Yes	...	No	Yes	No	No	Two year	No	Bank transfer	117.75	8242.50	No
3302	CUST3302	Male	1	Yes	Yes	68	Yes	No	DSL	Yes	...	Yes	Yes	No	No	Two year	No	Credit card	118.25	8041.00	Yes
3414	CUST3414	Male	1	No	No	71	No	No phone service	DSL	No	...	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	116.80	8292.80	No
3473	CUST3473	Female	0	No	No	68	Yes	Yes	DSL	Yes	...	No	No	No	Yes	Month-to-month	Yes	Bank transfer	118.49	8057.32	Yes
3707	CUST3707	Female	1	No	Yes	69	No	No phone service	Fiber optic	Yes	...	No	No	Yes	No	One year	No	Mailed check	117.38	8099.22	Yes
3775	CUST3775	Female	1	Yes	Yes	67	No	No phone service	DSL	Yes	...	Yes	Yes	Yes	No	Two year	Yes	Bank transfer	118.91	7966.97	Yes
3789	CUST3789	Male	1	Yes	No	71	No	No phone service	DSL	No	...	No	Yes	No	Yes	Two year	Yes	Mailed check	112.14	7961.94	No
4094	CUST4094	Female	0	Yes	Yes	69	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	119.99	8279.31	Yes
4437	CUST4437	Female	1	Yes	No	72	Yes	No	Fiber optic	No	...	Yes	Yes	No	Yes	Month-to-month	Yes	Bank transfer	112.80	8121.60	No
4638	CUST4638	Female	0	Yes	No	69	Yes	Yes	DSL	Yes	...	No	No	Yes	No	Two year	No	Credit card	114.42	7894.98	Yes
4718	CUST4718	Female	0	No	Yes	71	Yes	Yes	Fiber optic	Yes	...	Yes	Yes	No	No	Two year	No	Credit card	115.53	8202.63	No
4774	CUST4774	Male	0	No	Yes	68	No	No phone service	Fiber optic	No	...	No	No	Yes	Yes	One year	Yes	Credit card	117.36	7980.48	Yes
4872	CUST4872	Male	1	Yes	Yes	69	No	No phone service	DSL	No	...	No	No	No	Yes	Two year	No	Credit card	116.72	8053.68	No
5082	CUST5082	Male	0	No	No	71	Yes	No	Fiber optic	Yes	...	No	No	No	Yes	One year	Yes	Bank transfer	119.79	8505.09	No
5257	CUST5257	Male	0	No	Yes	68	No	No phone service	DSL	No	...	No	Yes	Yes	Yes	One year	Yes	Credit card	116.22	7902.96	No
5349	CUST5349	Male	1	No	Yes	72	Yes	No	Fiber optic	No	...	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card	117.35	8449.20	No
5476	CUST5476	Male	1	Yes	No	71	Yes	Yes	DSL	No	...	Yes	No	Yes	No	One year	Yes	Mailed check	118.74	8430.54	No
5704	CUST5704	Female	1	No	Yes	71	Yes	No	DSL	Yes	...	Yes	Yes	No	No	One year	No	Mailed check	113.59	8064.89	No

32 rows × 21 columns

###tRIMMING IQR score

# Using IQR for trimming, assuming we already have lower_bound and upper_bound calculated
trimmed_df = df[(df['TotalCharges'] <=upper_bound) & (df['TotalCharges'] >=lower_bound)]

trimmed_df.shape

(5848, 21)

##Capping using IQR
df['TotalCharges'] = np.where(
    df['TotalCharges']>upper_bound,
    upper_bound,
    np.where(
        df['TotalCharges']<lower_bound,
        lower_bound,
        df['TotalCharges']
    )
)

df.describe().round(2)

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges
count	5880.0	5880.00	5880.00	5880.00
mean	0.5	36.55	70.16	2565.32
std	0.5	20.91	28.80	1905.67
min	0.0	1.00	20.00	20.03
25%	0.0	18.00	45.72	1020.22
50%	1.0	37.00	70.16	2136.44
75%	1.0	55.00	95.46	3767.66
max	1.0	72.00	119.99	7888.84